#supervised learning data value
304 related articles
·7 min
Getting Started with Codex: Installation and Usage Guide for OpenAI's AI Coding Agent
A comprehensive guide to OpenAI's new AI coding agent Codex: from concept and comparison of its four forms, to installing Git/Node.js/VS Code, configuring the API Key, and creating a workspace.
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·6 min
The AI Industrial Age Arrives: Compute Wars, Chip Reconstruction, and the GPT-5.6 Cheating Scandal Explained
Five key AI industry trends: Doubao surpasses 180 trillion daily calls, OpenAI's in-house AI chip, NVIDIA's $3-4 trillion compute forecast, China catching up, and the GPT-5.6 cheating scandal.
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·8 min
LangChain Crash Course: A Complete Roadmap from Zero to Agent Development
A systematic roadmap from LangChain and LangGraph to multi-agent development, covering RAG, Tool Calling, MCP, and more, helping developers break into AI app development.
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·3 min
Grok 4.5 Review: A Full Breakdown of Elon Musk's Most Powerful AI Coding Model
xAI releases Grok 4.5, ranking #1 on SWE Marathon and outperforming Claude Opus. Explore benchmark scores, Agent capabilities, free access, and CLI installation.
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Kronos Financial Foundation Model: Usi…
·7 min
Kronos Financial Foundation Model: Using AI to Read the Market Language of Candlesticks
Kronos is the first open-source foundation model treating candlestick data as the "language of financial markets," using an autoregressive Transformer and earning 32K GitHub Stars. A deep dive into its principles, applications, and limits.
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·7 min
The Complete Guide to LLM Application Engineer Interviews: Six High-Frequency Agent Topics
A focused guide to core LLM application engineer interview topics, covering agent architecture, Multi-Agent, Langfuse evaluation, security, and RAG optimization.
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·7 min
The Complete Guide to LLM Application Engineer Interviews: Six High-Frequency Topics in the Agent Domain
A focused guide to the core interview topics for LLM application engineers, covering agent architecture, Multi-Agent, Langfuse evaluation & tracing, security, and RAG optimization.
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·6 min
The AI Builder's Handbook: A Complete Guide to LLM Principles, RAG Architecture, and Agent Design
From the autocomplete nature of LLMs, tokens, and context windows to RAG vector databases, the MCP protocol, and AI agent loop design — this article uses vivid analogies to unpack the reality of AI engineering.
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·3 min
Agent Fine-Tuning in Practice: A Complete Guide to Training Your Own AI Agent
A deep dive into Agent Tuning: from LLM hallucination and staleness issues to RAG vs. Agent architecture, the 4-step fine-tuning process, and cost analysis for building your own AI agent.
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·3 min
Can Manually Vectorized Data Train AI Models? A Must-Read Guide for Practitioners
Can manually vectorized paired data train AI models? This guide explores the value, technical feasibility, and monetization paths for practitioners holding professional domain data.
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·3 min
AI Development for Java Engineers: A Complete Guide to LangChain4j Agents
Java developers can build AI apps too! Learn LangChain4j fundamentals including RAG, Agents, Function Calling, and hands-on projects — no Python required.
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·4 min
UK Sovereign AI: How Cosine AI Is Punching Above Its Weight Against American Compute Dominance
Cosine AI founder reveals how the UK's first sovereign LLM is being built — from government compute grants and RL credit attribution to multi-agent orchestration and synthetic data pipelines.
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·3 min
Qwen's Six-Generation Evolution: A Complete Breakdown from Architecture Innovations to the Reasoning Race
From Qwen1 to Qwen3-2507: a complete breakdown of six generations of evolution over two years — GQA, MoE, GRPO, dynamic thinking, and the core shift from architecture to reasoning.
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·3 min
Career Pivot at 47: Midlife Job Anxiety and Redefining Competence in the Age of AI
A 47-year-old engineer who pivoted to data science faces re-employment struggles — a mirror of AI-era anxiety: does using AI count as coding? How to break the midlife career trap?
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·3 min
LangChain + MCP + Agent: A Complete Guide to Enterprise-Grade AI Agent Development
A deep dive into LangChain, LangGraph, MCP, and enterprise AI Agent development: covering Streamable HTTP updates, DeepSeek R1 Function Calling limits, and Qwen3 agent capabilities.
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Can LLMs Really Understand Computer Ar…
·3 min
Can LLMs Really Understand Computer Architecture Papers? A Deep Dive into Comprehension Capabilities
Can LLMs truly understand computer architecture papers? This article analyzes core challenges—from surface pattern matching to deep reasoning—and defines their capability limits for researchers.
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·3 min
Building a Video Generation Agent: Lessons Learned from Brief to Storyboard
A developer's hands-on account of building a brief-to-storyboard video Agent: JSON errors, missing fields, pacing issues — and how JSON Schema, retry loops, and MCP tools solved them.
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·2 min
Claude Sonnet 5 Deep Dive: Near-Opus Performance but Higher Costs — Is It Worth the Upgrade?
Claude Sonnet 5 benchmarked: near-Opus 4.8 performance but poor token efficiency makes it pricier than the flagship. Full analysis of pricing, safety tradeoffs, and real-world results.
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·3 min
Understanding Large Language Models from Scratch: The Complete AI Evolution from Deep Blue to DeepSeek
A beginner-friendly guide clarifying AI, machine learning, deep learning, and LLMs — tracing the evolution from Deep Blue to AlphaGo, ChatGPT, and DeepSeek.
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·4 min
Understanding Transformer From the Ground Up: How Language Models Predict the Next Word
From Tokenization and Embedding to the Attention mechanism, this article systematically breaks down how Transformer works — and how ChatGPT turns input text into next-token probabilities.
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